@article {Yousfi2021588,
	title = {An Efficient Holistic Schema Matching Approach},
	journal = {Communications in Computer and Information Science},
	volume = {1350},
	year = {2021},
	note = {cited By 0},
	pages = {588-601},
	abstract = {Schema matching allows a certain way of communication between heterogeneous, autonomous and distributed data sources. We choose the matching approach depending on the number of sources we wish to integrate: pairwise matching approaches for a small to a medium total number of data sources, and holistic matching approaches for a big to a huge number of data sources. Nevertheless, current matching approaches were proven to achieve a very moderate matching accuracy. Moreover, holistic matching approaches operate in a series of two-way matching steps. In this paper, we present hMatcher, an efficient holistic schema matching approach. To execute holistic schema matching, hMatcher captures frequent schema elements in the given domain prior to any matching operation. To achieve high matching accuracy, hMatcher uses a context-based semantic similarity measure. Experimental results on real-world domain show that hMatcher performs holistic schema matching properly, and outperforms current matching approaches in terms of matching accuracy. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Computer science, Computers, Context-based semantics, Current matching, Distributed data sources, Holistic schema matching, Number of sources, Pairwise matching, Real world domain, Schema matching, Semantics},
	doi = {10.1007/978-3-030-69143-1_45},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102749207\&doi=10.1007\%2f978-3-030-69143-1_45\&partnerID=40\&md5=d28b470cb44f6ca89c012259c51ec4f6},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
@conference {Yousfi2020,
	title = {CSSM: A context-based semantic similarity measure},
	booktitle = {2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {Semantic similarity is very critical in applications that manipulate data across heterogeneous, autonomous and distributed data sources. It helps connect these data sources. Nevertheless, current semantic similarity approaches were proven to achieve a very moderate accuracy. Furthermore, they are merely applicable when we wish to determine the semantic similarity between words. In this paper, we present CSSM, a Context-based Semantic Similarity Measure. Experimental results on Miller Charles benchmark dataset show that CSSM performs semantic similarity comparisons properly, and obtains high accuracy. {\textcopyright} 2020 IEEE.},
	keywords = {Benchmark datasets, Computer science, Computers, Context-based semantics, Control engineering, Data-sources, Distributed data sources, Electronics engineering, Electronics industry, High-accuracy, Semantic similarity, Semantic similarity approaches, Semantics},
	doi = {10.1109/ICECOCS50124.2020.9314376},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100120972\&doi=10.1109\%2fICECOCS50124.2020.9314376\&partnerID=40\&md5=3191da8d69fa40844c69e41c268c5a17},
	author = {Yousfi, A. and El Yazidi, M.H. and Zellou, A.}
}
